Biology subjects

Li, C.

Publications and source records attributed to Li, C..

At least 649 records · Page 36Linked to original sources

bioRxiv2023

SpaceWalker: Interactive Gradient Exploration for Spatial Transcriptomics Data

Spatial transcriptomics (ST) enables profiling the expression of hundreds of genes in tissue sections, down to the level of single cells in their tissue environment. The gradient structure of ST data is particularly interesting for tissue biology, since spatial gene expression gradients often represent tissue compartment edges, whereas in the single-cell transcriptomic domain, gene expression gradients may represent cell type differences and smooth phenotypic transitions. Various computational approaches have been developed to extract information from either the spatial domain or gene expression domain individually. However, integrative biological interpretation of expression gradients in single cell and ST data spaces remains challenging. Many prior spatial transcriptomics analysis pipelines are script-based, lack interactive exploration facilities, and do not have specific facilities for automatic identification of localized expression gradients. Here, we present SpaceWalker, a visual analytics tool for exploring the local gradient structure of ST data. The user is guided by the local intrinsic dimensionality of the high-dimensional data to define seed locations, from which a flood-fill algorithm approximates k-nearest neighbor subgraph topology on the fly. In several use cases, we demonstrate that the spatial projection of these local subgraphs highlights tissue architectural features, and that interactive retrieval of gene expression gradients in the spatial and transcriptomic domains confirms known biology, and provides additional insights into the tissue architecture. We also show that SpaceWalker generalizes to several different ST protocols, and scales well to large, multi-slice, whole-brain ST data, while maintaining real-time interaction performance.

bioinformatics↗

bioRxiv2023

Identification of RNA-Binding Protein Targets with HyperTRIBE in Saccharomyces cerevisiae

As a mater regulator in cells, RNA-binding protein (RBP) plays critical roles in organismal development, metabolism and various diseases. It controls gene expression at multiple levels mostly by specific recognition of target RNA. The traditional CLIP-seq method to detect transcriptome-wide RNA targets of RBP is less efficient in yeasts due to their cell walls. Here, we established an efficient HyperTRIBE (Targets of RNA-binding proteins Identified By Editing) in yeast, by fusing a RBP to the hyper active catalytic domain of human RNA editing enzyme ADAR2 and expressing the fusion protein in yeast cells. The target transcripts of RBP were marked with new RNA editing events and identified by high-throughput sequencing. We successfully applied TRIBE to identifying the RNA targets of two yeast RBPs, KHD1 and BFR1. The antibody-free HyperTRIBE has competitive advantages including low background, high sensitivity and reproducibility, and a simple library preparation procedure, which provides a reliable strategy for RBP target identification in Saccharomyces cerevisiae.

molecular biology↗

bioRxiv2023

The Role of Terminal Uridyl Transferases in the Circadian Rhythm

The 3 terminal oligo-uridylation, a post-transcriptional mRNA modification, is conserved among eukaryotes and drives mRNA degradation, thereby affecting several key biological processes such as animal development and viral infection. Our TAIL-seq experiment of mouse liver mRNA collected from six zeitgeber times reveals transcripts with rhythmic poly(A) tail lengths and demonstrates that overall 3 terminal uridylation frequencies at mRNA poly(A) tail very-ends undergo rhythmic change. Consistently, major terminal uridylyl transferases, TUT4 and TUT7, have cycling protein expression in mouse liver corresponding to 3 terminal uridylation rhythms, indicating that the cycling expression of TUTases correlates with the rhythmic pattern of uridylation. Furthermore, the double knockdown of TUT4 and TUT7 in U2OS cells lengthens the circadian period and decreases the rhythmic amplitude of clock gene expression. Our work thoroughly profiles the dynamic changes in poly(A) tail lengths and terminal modifications and uncovers uridylation as a post-transcriptional modulator in the mammalian circadian clock.

molecular biology↗

bioRxiv2023

Distinct activation mechanisms of β-arrestin 1 revealed by 19F NMR spectroscopy

{beta}-Arrestins ({beta}arrs) are functionally versatile proteins that play critical roles in the G-protein-coupled receptor (GPCR) signaling pathways. While the classical theory of GPCR-mediated {beta}arr activation centers around the formation of a stable complex between {beta}arr and the phosphorylated receptor tail, emerging evidences highlight the indispensable contribution from membrane lipids for many receptors. Due to the intrinsic complexity of {beta}arr conformational dynamics, detailed molecular mechanisms of its activation by different binding partners remain elusive. Herein we present a comprehensive study of the structural changes of {beta}arr1 in critical structural regions during activation using 19F NMR method. We demonstrate that phosphopeptides derived from different classes of GPCRs show distinct abilities in inducing {beta}arr1 activation. We further show that the membrane phosphoinositide PIP2 independently modulates {beta}arr1 conformational dynamics without displacing its autoinhibitory carboxyl tail, leading to a distinct partially activated state. Our results delineate two activation mechanisms of {beta}arr1 by different binding partners, uncovering a highly multifaceted conformational energy landscape for this protein family.

biochemistry↗

bioRxiv2023

Split-Transformer Impute (STI): Genotype Imputation Using a Transformer-Based Model

MotivationDespite recent advances in sequencing technologies, genome-scale datasets continue to have missing bases and genomic segments. Such incomplete datasets can undermine downstream analyses, such as disease risk prediction and association studies. Consequently, the imputation of missing information is a common pre-processing step for which many methodologies have been developed. However, the imputation of genotypes of certain genomic regions and variants, including large structural variants, remains a challenging problem. ResultsHere, we present a transformer-based deep learning framework, called a split-transformer impute (STI) model, for accurate genome-scale genotype imputation. Empowered by the attention-based transformer model, STI can be trained for any collection of genomes automatically using self-supervision. STI handles multi-allelic genotypes naturally, unlike other models that need special treatments. STI models automatically learned genome-wide patterns of linkage disequilibrium (LD), evidenced by much higher imputation accuracy in high LD regions. Also, STI models trained through sporadic masking for self-supervision performed well in imputing systematically missing information. Our imputation results on the human 1000 Genomes Project show that STI can achieve high imputation accuracy, comparable to the state-of-the-art genotype imputation methods, with the additional capability to impute multi-allelic structural variants and other types of genetic variants. Moreover, STI showed excellent performance without needing any special presuppositions about the patterns in the underlying data when applied to a collection of yeast genomes, pointing to easy adaptability and application of STI to impute missing genotypes in any species.

genomics↗

bioRxiv2023

Broadly effective ACE2 decoy proteins protect mice from lethal SARS-CoV-2 infection

As SARS-CoV-2 variants have been causing increasingly serious drug resistance problem, development of broadly effective and hard-to-escape anti-SARS-CoV-2 agents is in urgent need. Here we describe further development and characterization of two SARS-CoV-2 receptor decoy proteins, ACE2-Ig-95 and ACE2-Ig-105/106. We found that both proteins had potent and robust in vitro neutralization activities against diverse SARS-CoV-2 variants including Omicron, with an average IC50 of up to 37 pM. In a stringent lethal SARS-CoV-2 infection mouse model, both proteins lowered lung viral load by up to [~]1000 fold, prevented the emergence of clinical signs in >75% animals, and increased animal survival rate from 0% (untreated) to >87.5% (treated). These results demonstrate that both proteins are good drug candidates for protecting animals from severe COVID-19. In a head-to-head comparison of these two proteins with five previously-described ACE2-Ig constructs, we found that two of these constructs, each carrying five surface mutations in the ACE2 region, had partial loss of neutralization potency against three SARS-CoV-2 variants. These data suggest that extensively mutating ACE2 residues near the RBD-binding interface should be avoided or performed with extra caution. Further, we found that both ACE2-Ig-95 and ACE2-Ig-105/106 could be produced to gram/liter level, demonstrating the developability of them as biologic drug candidates. Stress-condition stability test of them further suggests that more studies are required in the future to improve the stability of these proteins. These studies provide useful insight into critical factors for engineering and preclinical development of ACE2 decoys as broadly effective therapeutics against diverse ACE2-utilizing coronaviruses. Abstract ImportanceEngineering soluble ACE2 proteins that function as a receptor decoy to block SARS-CoV-2 infection is a very attractive approach to broadly effective and hard-to-escape anti-SARS-CoV-2 agents. This study here describes development of two antibody-like soluble ACE2 proteins that broadly block diverse SARS-CoV-2 variants including Omicron. In a stringent COVID-19 mouse model, both proteins successfully protected >87.5% animals from lethal SARS-CoV-2 infection. In addition, a head-to-head comparison of the two constructs developed in this study with five previously-described ACE2 decoy constructs were performed here. Two previously-described constructs with relatively more ACE2-surface mutations were found with less robust neutralization activities against diverse SARS-CoV-2 variants. Further, the developability of the two proteins as biologic drug candidates was also assessed here. This study provides two broadly anti-SARS-CoV-2 drug candidates and useful insight into critical factors for engineering and preclinical development of ACE2 decoy as broadly effective therapeutics against diverse ACE2-utilizing coronaviruses. TweetTwo antibody-like ACE2 decoy proteins could block diverse SARS-CoV-2 variants and prevent animals from severe COVID-19.

microbiology↗

bioRxiv2023

Defective mechanosensory transduction of the new inner hair cells prevents hearing recover in the damaged cochlea

ABSTRACTHearing loss is a major health problem worldwide. Numerous attempts at regenerating functional hair cells (HCs) have been unsuccessful, but little is known about the main barrier that prevents us from achieving it and improving the hearing ability after damage. Here, we developed an in vivo genetic mouse model, by which the inner HCs (IHCs), the primary sound receptors innervated by the auditory neurons, were specifically damaged and the neighboring nonsensory supporting cells (SCs) were transformed into IHCs by ectopic expression of transient Atoh1 and permanent Tbx2. Despite [~]477 new IHCs were regenerated per cochlea and their differentiation status was more advanced than reported previously, no significant hearing improvement was achieved. By taking advantage of this unique model, we further found that the new IHCs expressed the functional marker vGlut3, harbored the similar transcriptomic profiles and electrophysiological properties as the endogenous IHCs. However, the mechanosensory transduction (MET) current could not be recorded in the new IHCs. Thus, our study indicated that the defective MET should be the main barrier that stops us from restoring the hearing capacity in the damaged cochlea and would pave the way for regenerating IHCs in vivo.

neuroscience↗

bioRxiv2023

Semi-conservative transmission of DNA N6-adenine methylation in a unicellular eukaryote

While DNA N6-adenine methylation (6mA) is best known in prokaryotes, its presence in eukaryotes has generated great interest recently. Biochemical and genetic evidence supports that AMT1, a MT-A70 family methyltransferase (MTase), is crucial for 6mA deposition in unicellular eukaryotes. Nonetheless, 6mA transmission mechanism remains to be elucidated. Taking advantage of Single Molecule Real-Time Circular Consensus Sequencing (SMRT CCS), here we provide definitive evidence for semi-conservative transmission of 6mA, showcased in the unicellular eukaryote Tetrahymena thermophila. In wildtype (WT) cells, 6mA occurs at the self-complementary ApT dinucleotide, mostly in full methylation (full-6mApT); hemi-methylation (hemi-6mApT) is transiently present on the parental strand of newly replicated DNA. In {Delta}AMT1 cells, 6mA predominantly occurs as hemi-6mApT. Hemi-to-full conversion in WT cells is fast, robust, and likely processive, while de novo 6mA deposition in {Delta}AMT1 cells is slow and sporadic. In Tetrahymena, regularly spaced 6mA clusters coincide with linker DNA of the canonical nucleosome arrays in the gene body. Importantly, in vitro methylation of human chromatin by reconstituted AMT1 complex recapitulates preferential targeting of hemi-6mApT sites in linker DNA, supporting AMT1s intrinsic and autonomous role in maintenance methylation. We conclude that 6mA is transmitted by a semi-conservative mechanism: full-6mApT is split by DNA replication into hemi-6mApT, which is restored to full-6mApT by AMT1-dependent maintenance methylation. Our study dissects AMT1-dependent maintenance methylation and AMT1-independent de novo methylation, reveals a molecular pathway for 6mA transmission with striking similarity to 5-methyl cytosine (5mC) transmission at the CpG dinucleotide, and establishes 6mA as a bona fide eukaryotic epigenetic mark.

molecular biology↗

bioRxiv2023

simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data

Single-cell chromatin accessibility sequencing (scCAS) technology provides an epigenomic perspective to characterize gene regulatory mechanisms at single-cell resolution. With an increasing number of computational methods proposed for analyzing scCAS data, a powerful simulation framework is desirable for evaluation and validation of these methods. However, existing simulators generate synthetic data by sampling reads from real data or mimicking existing cell states, which is inadequate to provide credible ground-truth labels for method evaluation. We present simCAS, an embedding-based simulator, for generating high-fidelity scCAS data from both cell-wise and peak-wise embeddings. We demonstrate simCAS outperforms existing simulators in resembling real data and show that simCAS can generate cells of different states with user-defined cell populations and differentiation trajectories. Additionally, simCAS can simulate data from different batches and encode user-specified interactions of chromatin regions in the synthetic data, which provides ground-truth labels more than cell states. We systematically demonstrate that simCAS facilitates the benchmarking of four core tasks in downstream analysis: cell clustering, trajectory inference, data integration, and cis-regulatory interaction inference. We anticipate simCAS will be a reliable and flexible simulator for evaluating the ongoing computational methods applied on scCAS data. AvailabilitysimCAS is freely available at https://github.com/Chen-Li-17/simCAS.

bioinformatics↗

bioRxiv2023

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics

SO_SCPLOWUMMARYC_SCPLOWEstimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimers disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available at https://github.com/chunlinli/sumdag.

molecular biology↗

bioRxiv2023

How spotted lanternflies get back on their feet: 3D mechanical modeling explains terrestrial self-righting strategies

The ability to upright quickly and efficiently when overturned on the ground (terrestrial self-righting) is crucial for living organisms and robots. Previous studies have mapped the diverse behaviors used by various animals to self-right on different substrates, and proposed physical models to explain how body morphology can favor specific self-righting methods. However, to our knowledge no studies have quantified and modeled all of an animals limb motions during these complicated behaviors. Here, we studied terrestrial self-righting for nymphs of the invasive spotted lanternfly (Lycorma delicatula), an insect that must frequently recover from being overturned after jumping and falling in its native habitat. These nymphs self-righted successfully in 92-100% of trials on three substrates with different friction and roughness, with no significant difference in the time or number of attempts required. They accomplished this using three stereotypic sequences of movements. To understand these motions, we combined 3D poses tracked on multi-view high-speed video with articulated 3D models created using photogrammetry and Blender rendering software. The results were used to calculate the mechanical properties (e.g., potential and kinetic energy, angular speed, stability margin, torque, force, etc.) of these insects during righting trials. We used an inverted physical pendulum model (a "template") to estimate the kinetic energy available in comparison to the increase in potential energy required to flip over. While these insects began righting using primarily quasistatic motions, they also used dynamic leg motions to achieve final tip-over. However, this template did not describe important features of the insects center of mass trajectory and rotational dynamics, necessitating the use of an "anchor" model comprising the 3D rendered body model and six articulated two-segment legs to model the bodys internal degrees of freedom and capture the role of the legs contribution to inertial reorientation. This anchor elucidated the sequence of highly coordinated leg movements these insects used for propulsion, adhesion, and inertial reorientation during righting, and how they frequently pivot about a body contact point on the ground to flip upright. In the most frequently used method, diagonal rotation, these motions allowed nymphs to spin their bodies to upright with lower force with a greater stability margin compared to the other less frequently-used methods. We provide a concise overview of necessary background on 3D orientation and rotational dynamics, and the resources required to apply these low-cost modeling methods to other problems in biomechanics.

biophysics↗

bioRxiv2023

Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting

Mediation analysis is a useful tool in investigating how molecular phenotypes such as gene expression mediate the effect of exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects in opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, we recently proposed a variance-based R-squared total mediation effect measure that relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In the work described herein, we formulated a more efficient two-stage, cross-fitted estimation procedure for the R2 measure. To avoid potential bias, we performed iterative Sure Independence Screening (iSIS) in two subsamples to exclude the non-mediators, followed by ordinary least squares regressions for the variance estimation. We then constructed confidence intervals based on the newly derived closed-form asymptotic distribution of the R2 measure. Extensive simulation studies demonstrated that this proposed procedure is much more computationally efficient than the resampling-based method, with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and identified the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol level. The proposed estimation procedure is implemented in R package CFR2M.

molecular biology↗

bioRxiv2023

Neutralization of SARS-CoV-2 BQ.1.1 and XBB.1.5 by Breakthrough Infection Sera from Previous and Current Waves in China

SARS-CoV-2 is continuing to evolve and diversify, with an array of various Omicron sub-lineages, including BA.5, BA.2.75, BN.1, BF.7, BQ.1, BQ.1.1, XBB and XBB.1.5, now circulating globally at recent time. In this study, we evaluated the neutralization sensitivity of a comprehensive panel of Omicron subvariants to sera from different clinical cohorts, including individuals who received homologous or heterologous booster vaccinations, vaccinated people who had Delta or BA.2 breakthrough infection in previous waves, and patients who had BA.5 or BF.7 breakthrough infection in the current wave in China. All the Omicron subvariants exhibited substantial neutralization evasion, with BQ.1, BQ.1.1, XBB.1, and XBB.1.5 being the strongest escaped subvariants. Sera from Omicron breakthrough infection, especially the recent BA.5 or BF.7 breakthrough infection, exhibited higher neutralizing activity against all Omicron sub-lineages, indicating the chance of BA.5 and BF.7 being entirely replaced by BQ or XBB subvariants in China in a short-term might be low. We also demonstrated that the BQ and XBB subvariants were the most resistant viruses to monoclonal antibodies. Continuing to monitor the immune escape of SARS-CoV-2 emerging variants and developing novel broad-spectrum vaccines and antibodies are still crucial.

immunology↗

bioRxiv2023

CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization

Circular RNAs (circRNAs) play a significant role in some diseases by acting as transcription templates. Therefore, analyzing the interaction mechanism between circRNA and RNA-binding proteins (RBPs) has far-reaching implications for the prevention and treatment of diseases. Existing models for circRNA-RBP identification most adopt CNN, RNN, or their variants as feature extractors. Most of them have drawbacks such as poor parallelism, insufficient stability, and inability to capture long-term dependence. To address these issues, we designed a Seq_transformer module to extract deep semantic features and then propose a CircRNA-RBP identification model based on Sequence Self-attention with Pre-normalization. We test it on 37 circRNA datasets and 31 linear RNA datasets using the same set of hyperparameters, and the overall performance of the proposed model is highly competitive and, in some cases, significantly out-performs state-of-the-art methods. The experimental results indicate that the proposed model is scalable, transformable, and can be applied to a wide range of applications without the need for task-oriented fine-tuning of parameters. The code is available at https://github.com/cc646201081/CircSSNN. Author summaryIn this paper, we propose a new method completely using the self-attention mechanism to capture deep semantic features of RNA sequences. On this basis, we construct a CircSSNN model for the cirRNA-RBP identification. The proposed model constructs a feature scheme by fusing circRNA sequence representations with statistical distributions, static local context, and dynamic global context. With a stable and efficient network architecture, the distance between any two positions in a sequence is reduced to a constant, so CircSSNN can quickly capture the long-term dependence and extract the deep semantic features. Experiments on 37 circRNA datasets show that the proposed model has overall advantages in stability, parallelism, and prediction performance. Keeping the network structure and hyperparameters unchanged, we directly apply CircSSNN to linRNA datasets. The favorable results show that CircSSNN can be transformed simply and efficiently without task-oriented tuning. In conclusion, CircSSNN can serve as an appealing circRNA-RBP identification tool with good identification performance, excellent scalability, and wide application scope, which is expected to reduce the professional threshold required for hyperparameter tuning in bioinformatics analysis.

bioinformatics↗

bioRxiv2023

A cingulate-hippocampal circuit mediates early depressive symptoms in Alzheimer's disease

AbstractDepressive symptoms are prevalent and precedes cognitive decline in Alzheimers disease (AD), which worsen the clinical outcome and severely challenge the life quality of the patients. However, the neural circuitry mediating mood disturbance in AD remains elusive. Here we report that the glutamatergic projection from the anterior cingulate cortex (ACC) to the ventral hippocampal CA1 (vCA1) acts as the neural substrate of depressive symptoms in AD. We systemically mapped axonal projection in early stage of 5xFAD mice (3 months), which accompanies depressive- like behavior in these animals and identified reduced axonal projection from ACC to vCA1. This is in accordance with reduced axonal calcium signals detected with in vivo fiber photometry. Chemogenetic or optogenetic reversal of ACC-vCA1 circuitry activity efficiently ameliorated the depressive-like behaviors as well as cognitive impairment in 5xFAD mice. We further identified the synaptic molecules neuregulin-1 (Nrg1) as one vital signal regulating the synaptic glutamate transmission from the ACC to the vCA1. Collectively, these results indicated ACC-vCA1 as a critical pathway that regulates emotional states in Alzheimer disease. Targeting cingulate cortices with brain stimulation may treat depression in AD.

neuroscience↗

bioRxiv2023

Theoretical understanding of evolutionary dynamics on inhomogeneous networks

Evolution is the main feature of all biological systems that allows populations to change their characteristics over successive generations. A powerful approach to understand evolutionary dynamics is to investigate fixation probabilities and fixation times of novel mutations on networks that mimic biological populations. It is now well established that the structure of such networks can have dramatic effects on evolutionary dynamics. In particular, there are population structures that might amplify the fixation probabilities while simultaneously delaying the fixation events. However, the microscopic origins of such complex evolutionary dynamics remain not well understood. We present here a theoretical investigation of the microscopic mechanisms of mutation fixation processes on inhomogeneous networks. It views evolutionary dynamics as a set of stochastic transitions between discrete states specified by different numbers of mutated cells. By specifically considering star networks, we obtain a comprehensive description of evolutionary dynamics. Our approach allows us to employ physics-inspired free-energy landscape arguments to explain the observed trends in fixation times and fixation probabilities, providing a better microscopic understanding of evolutionary dynamics in complex systems.

biophysics↗

bioRxiv2023

Metabolomics reveals nucleoside analogs for regulating mucosal-associated invariant T cell responses

Although mucosal-associated invariant T (MAIT) cells recognize riboflavin-like metabolites from Gram-negative bacteria, MAIT cell stimulation by broad bacterial families and mammalian cells suggests the existence of novel ligands from different biological sources. Here we established a comparative platform of functional metabolomics and used Mycobacterium tuberculosis as a model to characterize novel metabolites for MAIT cell activation. We extracted and fractionated small metabolites of M. tuberculosis using high-performance liquid chromatography, showing a different MAIT cell stimulation pattern of M. tuberculosis metabolite fractions in comparison with Escherichia coli fractions. Mass profiling predicted multiple nucleoside analogs enriched in a biologically active fraction of M. tuberculosis. Whereas the synthetic forms of these predicted M. tuberculosis nucleosides were unavailable, structural-based autodocking of analogous nucleosides conserved in mammals supported potential binding with MR1 protein. Indeed, functional assays of these conserved nucleosides demonstrated guanosine as a stimulator and deoxyformyluridine as an inhibitor of MAIT cell activation. Identification of bioactive nucleoside metabolites broadly conserved in bacterial and mammalian systems will facilitate an understanding of the regulatory roles of MAIT cells in infectious and inflammatory conditions.

immunology↗

bioRxiv2023

Multi-omics Comparison among Populations of Three Plant Sources of Amomi Fructus

Amomi Fructus (Sharen, AF) is a traditional Chinese medicine (TCM) from three source species (or subspecies) including Wurfbainia villosa var. villosa (WVV), W. villosa var. xanthioides (WVX) or W. longiligularis (WL). Among them, WVV has been transplanted from its top-geoherb region Guangdong to its current main production area Yunnan for more than 50 years in China. However, the genetic and transcriptomic differentiation among multiple AF source (sub)species and between the origin and transplanted populations of WVV is unknown. In our study, the observed overall higher expression of terpenoid biosynthesis genes in WVV than that of WVX supplied possible evidence for the better pharmacological effect of WVV. We also screened ten candidate borneol dehydrogenase (BDH) genes that potentially catalyzed borneol into camphor in WVV. The BDH genes may experience independent evolution after acquiring the ancestral copies and the followed tandem duplications might account for the abundant camphor content in WVV. Furthermore, four populations of WVV, WVX and WL are genetically differentiated and the gene flow from WVX to WVV in Yunnan contributed to the increased genetic diversity in the introduced population (WVV-JH) compared to its top-geoherb region (WVV-YC), which showed the lowest genetic diversity and might undergo genetic degradation. In addition, TPS and BDH genes were selected among populations of multiple AF source (sub)species and between the top-geoherb and non-top-geoherb regions, which might explain the metabolite difference of these populations. Our findings provide important guidance for the conservation, genetic improvement, industrial development of the three source (sub)species, and identifying top-geoherbalism with molecular markers and proper clinical application of AF.

evolutionary biology↗